gradient-spaces

This repo contains the pre-trained checkpoints released for: "CrossOver: 3D Scene Cross-Modal Alignment" (CVPR 2025, Highlight)

[NeurIPS'25] Rectified Point Flow: Generic Point Cloud Pose Estimation [](https://rectified-pointflow.github.io/) [](https://arxiv.org/abs/2506.05282) [](https://github.com/GradientSpaces/Rectified-Point-Flow) Rectified Point Flow (RPF) is a unified model that formulates pairwise point cloud registration and multi-part shape assembly as a single conditional generative problem. Given unposed point clouds, the method learns a continuous point-wise velocity field that transports noisy points toward their target positions, from which part poses are recovered. Checkpoints - `RPFbasefull.ckpt`: Complete model checkpoint for assembly generation - `RPFbasepretrain.ckpt`: Encoder-only checkpoint for overlap prediction Training Data | Dataset | Task | Part segmentation source | Parts per sample | |---|---|---|---| | IKEA-Manual | Shape Assembly | Defined by IKEA manuals | [2, 19] | | PartNet | Shape Assembly | Human-annotated parts | [2, 64] | | BreakingBad-Everyday | Shape Assembly | Simulated fractures via fracture-modes | [2, 49] | | Two-by-Two | Shape Assembly | Annotated by human | 2 | | ModelNet-40 | Pairwise Registration | Following Predator split | 2 | | TUD-L | Pairwise Registration | Real scans with partial observations | 2 | | Objverse | Overlap Prediction | Segmented by SAMPart3D | [3, 12] |

This repo contains the pre-trained checkpoints for: HouseTour: A Virtual Real Estate A(I)gent (ICCV 2025)